This book provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you.
This book provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you.
Michael N. Mitchell is a senior statistician working in the area of sleep research as well as working on prevention of child maltreatment with the Children's Data Network. He is the author of three other Stata Press books- A Visual Guide to Stata Graphics, Data Management Using Stata, and Stata for the Behavioral Sciences.
Inhaltsangabe
Introduction Continuous predictors: Linear Continuous predictors: Polynomials Continuous predictors: Piecewise models Continuous by continuous interactions Continuous by continuous by continuous interactions Categorical predictors Categorical by categorical interactions Categorical by categorical by categorical interactions Linear by categorical interactions Polynomial by categorical interactions Piecewise by categorical interactions Continuous by continuous by categorical interactions Continuous by categorical by categorical interactions Multilevel models Time as a continuous predictor Time as a categorical predictor Nonlinear models Complex survey data
Introduction Continuous predictors: Linear Continuous predictors: Polynomials Continuous predictors: Piecewise models Continuous by continuous interactions Continuous by continuous by continuous interactions Categorical predictors Categorical by categorical interactions Categorical by categorical by categorical interactions Linear by categorical interactions Polynomial by categorical interactions Piecewise by categorical interactions Continuous by continuous by categorical interactions Continuous by categorical by categorical interactions Multilevel models Time as a continuous predictor Time as a categorical predictor Nonlinear models Complex survey data
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